Keywords

1 Introduction

Biesta and Säfström (2011: 544) comment in the Manifesto for Education that ‘[m]odern education has been associated with the development of the modern welfare state. The early pragmatists of the North American melting pot already saw education as a springboard to a new and better society. Technology was to become the driving force whilst education was to repair the ground for such a new society … [T]he values and norms through which this brave new world would form itself were based on the power of technology to make human living smoother and more effective in achieving its aspirations’. These changes in modern education were meant to instigate the advent of critical and democratic citizens, forming better and more just societies—and this is in direct opposition to the authoritarian Fascists and Nazis (and Communists) experiences of the past (cf. Säfström 2004). It is clear in Biesta and Säfström’s Manifesto that there is a longstanding connection between technology and education, which has become increasingly stronger in recent decades due to fast-paced technological changes experienced by humanity. This acceleration is happening in tandem with the process of globalization, meaning that new discoveries and technological developments spread throughout the world very rapidly. Further, it is important to emphasize that the link between technology and education is not confined to the issue of improving learning in schools and the quality of education that is being provided in schools; rather, it is also connected, at least originally, to the idea of citizenship and the improvement of societies, which would become increasingly more democratic and just.

This understanding of education would be critical of and has repercussions to the current processes of technologization and learnification in education because they tend to overlook the importance of the right kind of relations between teacher and student, and between students in education. Moreover, the technologization process favours a diminished understanding of education as the mere learning of skills (i.e. Erziehung) and does not favour education as character formation (i.e. Bildung). This problem is compounded by the ‘learnification’ trend, a term coined by Gert Biesta, which fails to appreciate the importance of the role of the teacher and of teaching in the educational process. Given this situation, in this chapter, I provide a discussion on the technologization of education and its implication for education as Erziehung and Bildung; then, I engage with a thought experiment enquiring if the development of AI would one day be capable of fully replacing teachers in the classroom. I wish to emphasize that the position I am defending in this chapter is not that ‘we should not be using technology to aid teaching and learning in the classroom’; rather, the point I am making is that ‘we should not overlook the importance of relations between teacher and student, and between students in the classroom’.

2 Technologization of Education

If we look closely, the connection between technology and education is very complex and multifaceted because of the political, economic, social and pedagogical implications that the use of technology has in education. Generally speaking, it is understood that given that we live in ‘technological societies’, we must use technology to help with teaching and learning tasks, and in addition to this, learning about and using technologies must be an important part of the curriculum. It is also understood that the technologization of education will support students who often feel disadvantaged by the traditional educational system, improving their performances through access to computers, special softwares and Internet (Laura and Chapman 2009: 289). For instance, the issue of ‘technological inclusion’ of individuals by means of education has deep social, political and economic effects, such as individuals being fit to join the labour market and contribute to the economic development of societies; likewise, ‘technological exclusion’ presents us with serious social, political and economic problems, such as unemployment. In addition, the use of technology in education may change educational contexts, their geography, as well as the dynamics between individuals. In fact, Buchanan et al. (2015: 227) note that we have undergo a ‘digital turn’, and this is to say that digital technologies ‘are no longer simply something that students learn “about,” but are now something that they increasingly learn “with”’; digital competences are now imbedded in the curriculum, spanning across all levels of education, and embraced by countries worldwide. These substantial changes in education mean that research must also shift its focus to start ‘looking beyond learning’ (Selwyn 2010: 65; cf. Buchanan et al. 2015: 227), so that ‘wider political, social, and cultural contexts of the use of digital technologies’ are taken into account, identifying the implications of this technologization process for social justice and democracy (cf. Buchanan et al. 2015: 227)—and I would add and emphasize, as already mentioned in the introduction, the impact of this technologization of education on teacher–student and student-student relations.

In addition to this, the rapid increase in the use of new technologies in education ‘is definitely not what one would call a slow movement’ (Apple 1988: 151). These changes in schools and in educational systems are associated to a notion of progress and this might lead us to ask questions such as: ‘Whose idea of progress? Progress for what? And fundamentally … who benefits [from this progress]’? (Apple 1988: 151). These are important questions connected to the political economy of education, but it is just as necessary to ask other questions directly connected to changes in the classroom and within the school setting. This hegemonic trend focusing on the importance of technology for education has had a direct impact on teachers and teacher education because they are expected to combine students’ and their own development of i. ‘basic skills’ and ii. ‘creativity and intellectual excellence within a globally technological and economically demanding society’ (Laura and Chapman 2009: 290). However, ‘skills-based training, combined with ever- growing technologies, have overshadowed personal creativity, humour, imagination, intellectual excellence, dialogue, collaborative learning, compassion and spiritual sensitivity, which, in turn, has diminished our educational purpose’ [as teachers] (ibid.). Thus, the tension between the development of basic skills and of personal excellence has not been resolved successfully within the current educational context, which makes us wonder about how successful it has been in encouraging citizenship and the development of more democratic and just societies, as originally envisaged (cf. Biesta and Säfström 2011).

My argument here is that the technologization of education has had a deep impact on teachers and teaching because of its focus on education as Erziehung, or education as the learning of a skill or trade, to the detriment of education as Bildung, or education as character formation. It is arguable that this focus on education as Erziehung has serious implications for the social, political and ethical spheres of education because it interferes directly and negatively with the individual’s capacity to be someone who is concerned for others in the community, who engages with the various problematic issues of society, and who is aware of the impact of actions upon himself or herself, others and society as a whole—that is, the Bildung aspect of education.

Biesta provides us with a very useful discussion on the issue of Bildung.Footnote 1 Biesta (2002: 344) says:

A brief look at one possible history of Bildung shows that there is both an educational and a political dimension to it. On the one hand Bildung stands for an educational ideal that emerged in Greek society and that, through its adoption in Roman culture, humanism, neo-humanism and the Enlightenment became one of the central notions of the modern Western educational tradition. Central in this tradition is the question as to what constitutes an educated or cultivated human being. The answer to this question is not given in terms of discipline, socialisation or moralisation, i.e. as the adaptation to an existing ‘external’ order. Bildung refers to the cultivation of the inner life, i.e. the human mind or human soul….Since then Bildung has always also been self-Bildung.

This educational idea, Bildung, has implications for civil society, for the political arena, because if an individual is capable of thinking autonomously and pass judgement, then he is going to be critical of civil society. Bildung is directly connected to Citizenship (cf. Biesta 2002: 345).

Further, the focus on education as Erziehung has happened alongside a pedagogical shift from teaching to learning, the so-called process of ‘learnification’, a term coined by Gert Biesta, which promotes the idea that teaching should be primarily concerned with the creation of rich learning environments that are very often supported by various technologies, such as the use of computer programs and internet connection, to aid scaffolding learning (e.g. a computer program to help with the learning of ‘Logic’ or ‘Ancient Greek’). In doing so, this process of ‘learnification’ has also attacked the idea that ‘teachers have something to teach and that students have something to learn from their teachers’ (Biesta 2010, 2013: 451). Biesta (2015: 76) writes:

In the past decade I have written about a phenomenon which I have referred to as the ‘learnification’ of educational discourse and practice. ‘Learnification’ encompasses the impact of the rise of a ‘new language of learning’ on education. This is evident in a number of discursive shifts, such as the tendency to refer to pupils, students, children and even adults as ‘learners’; to redefine teaching as ‘facilitating learning’, ‘creating learning opportunities’, or ‘delivering learning experiences’ or to talk about the school as a ‘learning environment’ or ‘place for learning’. It is also visible in the way in which adult education has been transformed into lifelong learning in many countries.

The influence of constructivist psychological theories and of thinkers like Vygotsky and Bruner in this paradigm shift is quite evident, but also misguided—it is a misreading of constructivism. In this connection, and as we have mentioned before, valid constructivism does not portray the teacher as a facilitator. This can be demonstrated convincingly by referring to the sociocultural theory of Vygotsky and Bruner, which considers that the individual learns in the condition of a social being. Through education, the subject receives models and cognitive supports that help him acquire certain knowledge. The teaching and learning process, viewed from this perspective, uses an instrument that plays a fundamental role: language. It has two functions, one communicative and the other cognitive. Communicative because, through language, those who teach and those who learn exchange their thoughts. Cognitive because it is the vehicle through which the child internalizes the concepts of their culture. This theoretical model is important insofar as it gives a central role to the social aspects of learning (Lacasa 1994). Following on from this, to understand the individual aspects of the construction of knowledge, it is fundamental to understand the social relations in which the individual develops. Vygotsky considers that the transition from the social to the individual implies a transformation. To explain this process of change, he elaborated the concepts of ‘Internalization’, ‘Zone of Proximal Development’ (ZPD) and mediation of the ‘More Knowledgeable Other (MKO)’ (Vygotsky 1995).

Coll (1990), a prominent commentator on the constructivist school, helps us to understand the process of teaching and learning from the perspective of sociocultural theory. He states that the constructivist conception is organized around three fundamental ideas:

  1. 1.

    The student is ultimately responsible for his or her own learning process. It is the student who constructs knowledge and no one can replace him in this task.

  2. 2.

    Students internalize, construct or reconstruct objects of knowledge that had already been socially elaborated by his society.

  3. 3.

    This situation of pre-existing and accepted cultural knowledge has implications for the role of the teacher. The teacher’s function cannot be limited solely to creating the optimal conditions for the student to realize their own individual rich and diverse mental construction. The teacher, as the More Knowledgeable Other (MKO), must manage and guide the student activity so that their developing comprehension progressively understands the meanings and representations of the culture. This guidance occurs through the Zone of Proximal Development (ZPD), establishing what the student already knows, and what can be learned with the help of the More Knowledgeable Other (cf. Guilherme et al. 2017).

However, this gives rise to a tension in what a teacher is and what teaching entails, because the teacher, by definition, is someone who has something to teach students, and not merely a facilitator of the learning process as the misguided understanding of constructivism would have (cf. Guilherme 2014: 252–253). In connection to this, Guilherme (2014: 256) argued whilst commenting on Martin Buber’s philosophy of education that:

Buber suggests that there is something that is essential to education; that is, the act of teaching must fundamentally entail revealing something that was hidden from the student, the Other…It is important to note…that this revelation does not occur just at the Erziehung level, when the student in a ‘eureka’ moment grasps how to perform a task successfully (e.g. how to do additions), it also happens at the Bildung level, when the individual understands the importance, the ethical weight, of being a moral being (e.g. the serious consequences of lying).

Hence, I argue that as a consequence of the diminished understanding of education as Erziehung, because of both the modern technologization trend and of the failure to appreciate the importance of the role of the teacher and of teaching due to the learnification process, there has been a significantly negative impact on the relations between teachers and students, and between students, in education. I believe this is something that is often overlooked by both educators and policy makers. In connection with this, several studies have established the importance of the quality of relationships between students and teachers for issues of personal self-esteem, motivation to learn and confidence in facing new challenges, all of which play a crucial role in overall academic achievement (Laura and Chapman 2009: 290). For instance, McDevitt et al. (2013: 15; 456) note that:

[R]eciprocal relationships exist between children and their environments…[I]f parents and teachers develop mutually respectful relationships, they may exchange information and together reinforce their support for a child. If parent-teacher relationships are poor, they may blame each other for a struggling child´s limitations, with the result that no one take responsibility to teach the child needed skills….[Further], when caregivers are kind and responsive, children begin to trust them and gain confidence in their own abilities. We learn that good relationships help children express their emotions productively and blossom into healthy, one-of-a-kind personalities. Finally, we see that educators can contribute immensely to children´s healthy emotional development.

It is thus very ironic, given that relationships are something very important in education, that the impact of the technologization of education and its potential depersonalization of the classroom is not discussed in more detail and philosophically questioned. It seems that in some quarters, we have been too ready to accept the successes of technology in education because it is very much part of the hegemonic discourse without being critical about it, without questioning its possible hindrances, and this might be the case because it is possible that technology has become the very standard for measuring progress and success, and therefore, the appropriate way of resolving problems, including pedagogical ones (cf. Laura and Chapman 2009: 291). In connection to this criticism, Warschauer et al. (2004: 584–585) noted in a study on computer and Internet connections in American schools, particularly in low-SES schools, that:

[T]here is no single digital divide in education but rather a host of complex factors that shape technology use in ways that serve to exacerbate existing educational inequalities. We found effective and less effective uses of information and communication technologies…in…schools. At the same time, we found no evidence to suggest that technology is serving to overcome or minimize educational inequalities within or across the…schools we examined. Rather, the evidence suggests the opposite: that the introduction of information and communication technologies in the…schools serves to amplify existing forms of [educational] inequalities.

3 Thought Experiment

Thought experiments are powerful philosophical devices that use the imagination to investigate a whole range of theoretical problems. They are commonly used in philosophy, economics and the sciences in general. Kuhn (1977: 241; 261) commented that they are ‘potent tool[s] for increasing our understanding … Historically their role is very close to the double played by actual laboratory experiments and observations. First, thought experiments can disclose … failure[s] to conform to a previously held set of expectations. Second, they can suggest particular ways in which both expectation and theory must henceforth be revised’. Thus, through resourcing to thought experiment, I wish to investigate if the development of AI could one day successfully replace human teachers in the classroom.

AI research has taken generally speaking two interconnected approaches. The first approach, which is very ambitious, seeks to develop a computer program that successfully mimics human intelligence, and in so doing it seeks to find explanatory models for human cognition. The second approach is less bold and seeks to develop computer programs that deal with particular problems (e.g. drawing; chess game; learning a language) without referring to models of human cognition, but which nevertheless display highly intelligent behaviour (McCorduck 1988: 68; cf. also McCorduck 1979). The former aims to imbue computers with the virtue of intelligence with the objective that the computer might one day replace human beings, occupying bureaucratic positions in the armed forces or corporations; the latter envisages developing discreet computer programs that could serve to enhance human intelligence, assisting human beings to carry out certain tasks (cf. Mirowski 2003: 136). This means that AI can be understood in two ways:

  1. i.

    we can understand AI as a computer program that successfully mimics human cognition—this is that which I call a thick conception of AI.

  2. ii.

    we can conceive of AI as a computer program that deals with a particular aspect of knowledge in a highly intelligent way, aiding human beings to perform certain tasks—this is that which I call a thin conception of AI.

The terminology thick and thin conception of AI is also referred to in the literature as Weak and Strong AI. Weak AI is usually characterized by a simulation of human intelligence whilst in the case of Strong AI, there is an expectation of genuine understanding and the instantiation of other cognitive states, so that a machine is capable of conscious thought (al-Rifaie and Bishop 2015: 44). This can be further explained in terms of creativity. In the case of Weak AI, the machine is capable of exploring a particular simulation of human creativity, and it is not required to be completely autonomous and provide genuine understanding; and in the instance of Strong AI, there is an expectation that the machine is fully autonomous, have a genuine understanding and is capable of other cognitive states (cf. al-Rifaie and Bishop 2015: 45).

There is a long philosophical tradition discussing the possibility of AI, Weak and Strong. Alan Turing published ‘Computing Machinery and Intelligence’ in the prestigious Mind in 1950 (cf. Turing 1950) where he proposed that which became known as ‘The Turing Test’; that is to say: if a computer can pass for a human in an online chat, then we should understand that it displays intelligence. However, in 1980, John Searle challenged this notion, expanding on the discussion about AI, and arguing in his paper ‘Minds, Brains, and Programs’ (cf. Searle 1980) that it is impossible for computers to display intelligence, to understand a language and to think. And in 1999, he summarized his argument in a very elegant manner:

Imagine a native English speaker who knows no Chinese locked in a room full of boxes of Chinese symbols (a data base) together with a book of instructions for manipulating the symbols (the program). Imagine that people outside the room send in other Chinese symbols which, unknown to the person in the room, are questions in Chinese (the input). And imagine that by following the instructions in the program the man in the room is able to pass out Chinese symbols which are correct answers to the questions (the output). The program enables the person in the room to pass the Turing Test for understanding Chinese but he does not understand a word of Chinese (cf. Cole 2014).

Searle’s argument demonstrates the serious difficulties involved in trying to create a machine capable of Strong AI, and this was further emphasized by him in his paper ‘Why Dualism (and Materialism) Fail to Account for Consciousness’. Searle (2010: 17) says:

I demonstrated years ago with the so-called Chinese Room Argument that the implementation of the computer program is not by itself sufficient for consciousness or intentionality (Searle 1980). Computation is defined purely formally or syntactically, whereas minds have actual mental or semantic contents, and we cannot get from syntactical to the semantic just by having the syntactical operations and nothing else. To put this point slightly more technically, the notion “same implemented program” defines an equivalence class that is specified independently of any specific physical realization. But such a specification necessarily leaves out the biologically specific powers of the brain to cause cognitive processes. A system, me, for example, would not acquire an understanding of Chinese just by going through the steps of a computer program that simulated the behaviour of a Chinese speaker. (cf. Cole 2014)

All that said, I note that the contrast between thick conception and thin conception of AI (i.e. Weak/Strong AI) has different implications for education and I shall deal with these in turn. Let me deal with the thin conception of AI first.

The use of computer programs to help with teaching and learning is now quite ubiquitous in certain countries, especially in the Global North. These programs have been used to help with a whole range of teaching and learning activities, from aiding with the learning of a particular subject (e.g. ‘Logic’ or ‘Ancient Greek’), to exercise practices and drills (e.g. ‘Arithmetic’ or ‘Geometry’), to formative or summative tests. These are now used at all levels, from primary to postgraduate, and in a whole range of subjects, not just in the sciences but also in the arts and humanities. Many of these programs are AI in essence, fitting the thin conception, and working as instrumental tools that help students to learn their subjects (e.g. Arithmetic).Footnote 2

One such early AI program aimed at dealing with a particular aspect of knowledge, which fits the thin conception of AI, is AARON, a computer program endowed with ideas about plants, size and shape of human beings, and balance and symmetry in art. This program does thousand of drawings, it knows what it has drawn and will not repeat it unless asked otherwise—thus, focusing on mimicking human creativity. AARON was created by the artist Harold Cohen (cf. McCorduck 1988: 65–66), and one could envisage it as being used pedagogically, teaching students about certain aspects of drawing, such as human and plant physiology in art and balance and symmetry in composition. It is interesting to note that when questioned if AARON is just producing images or really creating a form of art, Harold Cohen responds that it is indeed art and comments that: ‘Within Western culture … we have always afforded the highest level of responsibility—and praise or blame—to the individual who works on the highest conceptual level. We may hear a hundred different performances of a Beethoven quartet without ever doubting that we were listening to Beethoven. We remember the names of architects, not those of the builders who made their buildings. And, particularly, we value those whose work leaves art in a different state to the state in which they found it” (McCorduck 1988: 81; cf. also McCorduck 1985). It is arguable that AARON can only create a particular form of image; that is, it can work only within a set paradigm. Unlike the human being, AARON cannot change its paradigm and develop a new innovative style of producing images; it cannot argue against or accept criticism against its work; it cannot provide a rationale for why it has chosen to produce a particular drawing, for what inspired it to do so, and this makes us question if it is really intelligent.

Similarly, the above criticisms could be raised against AI computer programs currently being used to help with the learning of other subjects such as logic, languages, geometry and so on. However, it is important to note that there have been some new and very interesting developments. Baker et al. (2018: 224) note that:

Intelligent Tutoring Systems (ITSs) are hypothesized to be a particularly good candidate from improvement by addressing the connections between affect, cognition, motivation, and learning…ITSs are a type of educational software that offer guided learning support to students engaged in problem-solving. Existing intelligent tutors tailor their support of students’ needs in a variety of ways, including identifying and correcting student errors…and promoting mastery learning through assessments of the probability that the student knows each skill relevant to the system.

Instances of these ITSs are (i) AutoTutor, a software focusing on Newtonian physics, (ii) The Incredible Machine (TIM), a simulation environment for logical puzzles; and (iii) Aplusix, an Algebra learning assistant (Baker et al. 2018: 232–233). Whilst it can be argued that these programs increase contact with the subject, help accessing topics, enable the possibility of doing exercises and drills, facilitate the identification of areas within the subject that require further work, and thus, ‘produce … learning gains … better than classroom teaching alone’ (Boulay and Luckin 2015: 6; cf. also Olney et al. 2012), they cannot, in the same way that AARON cannot, engage on a real dialogue with the student. That is, such programs cannot engage in a real debate over a point of contention, cannot argue against or accept criticism, cannot improvise and pursue a different (and interesting) avenue suggested by students, cannot change its working paradigm.

This means that for the self-taught student using such AI computer programs, the educational experience will be confined to a poorer and thinner understanding of education. Education is not just about the learning of a skill (i.e. Erziehung) but also about character formation (i.e. Bildung). In the classroom, the use of such AI programs would only become problematic if the role of the teacher is undermined, if the teacher is seen as a mere ‘facilitator’ due to the process of ‘learnification’ and to the belief that the process of ‘technologization’ will eventually provide all the answers. Certainly, some of those who understand that computers and Internet are the very expression of progressiveness in education might fail to see this problem because of their belief on the importance of ‘rich environments’ for students’ learning and that the teacher is a ‘facilitator’ of the process; however, as I have argued, this fails to understand the importance of relationships and human encounter for education. I maintain here that it is not the case that we should not be using technology to aid teaching and learning in the classroom, but at the same time, we should not overlook the importance of relations between teacher and students, and between students in the classroom. We must encourage and facilitate rich relations in education if the educational process is to be fully accomplished, not mere Erziehung and also developing into Bildung, so that teachers and students understand that their reflections and actions have an impact upon themselves, their societies and the world.

This brings me to the thick conception of AI, a computer program that successfully mimics human cognition, and the thought experiment inquiring if AI could one day substitute teachers in the classroom. Attempts to create such a computer program have so far been unsuccessful but we can imagine the possible creation of a successful program in the (near) future. Sci-Fi literature and cinema can provide us with some useful examples of this kind of AI, and examples of this are I Robot (2004), A.I. Artificial Intelligence (2001), Bicentennial Man (1999) and Ex Machina (2015) films. The main characters in these films are robots who are clearly capable of intelligent behaviour and meaningful interaction with human beings, providing us with fertile ground for our discussion.

In the case of I Robot, the robot character is bestowed with internal laws that prevent it ever harming human beings following from the three laws (i.e. (i) a robot may not injure a human being or, through inaction, allow a human being to come to harm; (ii) a robot must obey the orders given it by human beings, except where such orders would conflict with the First Law; (iii) a robot must protect its own existence as long as such protection does not conflict with the First or Second Laws (cf. Asimov 1950). However, the main robot character in I Robot is incapable of emotions, which makes us feel that it is intelligent but not human-like. Further, because of the internal laws in its programming, the robot is constrained in its capacity to choose otherwise, which contrasts with human beings as we can always choose otherwise, we can choose between A and B, and take responsibility for it, feeling good about our right choices and bad about our wrong ones.

We could envisage a computer program, and let us call it T for teacher, which is endowed with the same kind of AI capabilities as that of the robot in the I Robot film. T would be capable of displaying perfect intelligent behaviour, of teaching skills extremely well, of interacting meaningfully with its students, but it would not be capable of feeling emotions (which is arguably a major hindrance in the classroom), of truly connecting with its students (which is very problematic in education, at least insofar as Bildung is concerned). This is to say, as T is not capable of feeling emotions, it would be incapable of truly empathizing with its students in the classroom (e.g. an event has happened and this has had an effect on students) and of reading the mood of the class when teaching and adapting its performance accordingly (e.g. the topic might be considered boring by students and a particular effort to bring them on board might be necessary); these are all part of the ‘specialized tactics that human teachers apply effectively’ in the classroom, which are derived ‘from the conversational and social interactive skills used in everyday settings such as listening, eliciting, intriguing, motivating, cajoling, explaining, arguing and so on’ (Boulay and Luckin 2015: 4). Further, as T is incapable of truly connecting with its students, the kind of education it can provide will always be confined to the learning of a skill, Erziehung, and will never be capable of developing into character formation, Bildung.Footnote 3 On a practical level, we can envisage T having problems controlling the class through the use of voice (e.g. raising the voice slightly to catch the groups attention), look (e.g. glancing at a particular group of distracted students), and presence (e.g. controlling behaviour and drawing attention through one’s own presence in the classroom). This is at the heart of the ‘impoverished repertoire of teaching tactics and strategies available to’ A.I. educational systems ‘compared with human expert teachers’ (Boulay and Luckin 2015: 1; cf. also Carroll and McKendree 1987; Ohlsson 1987; Ridgway 1988).

However, it is conceivable that the AI program could eventually develop in the same way as the main characters in films such as Bicentennial Man (1999) and A.I. Artificial Intelligence (2001) and develop emotions and the capacity to engage in rich relations. In this case, it is conceivable that the objections raised above would not apply, but it raises serious questions and challenging problems for AI research; for instance: What is consciousness? What is it to be human? Flood (1951: 34; cited in Mirowski 2003: 137) notes:

[N]obody really knows anything about consciousness. Now the purpose of Robotology is to take a hard problem such as this one of consciousness, or a relatively easy one like the learning problem - I can feel the psychologists shudder as I say this - so that a mixed team can be truly scientific in their work on them. Robotology, then, is a way of solving the communication problem in the sense that we don’t just let people talk philosophy, or methodology, or just plain hot air; they must talk in terms of something to be put into the design of an object.

The question about the nature of consciousness is very problematic because as we come to see in Ex Machina (2015), the main robot character is so human-like that we start to empathize with it, to believe that when we are faced with it, we are faced with someone like us, with an equal. However, this is just appearances with no substance to it as at the end of the film, we discover that the main robot character only cares for continuing to exist, lacking a moral compass, ethical behaviour and ‘humanity’. The crucial issue then is not to successfully ‘mimic human consciousness’ as it happens in Ex Machina, but to find a way of enabling the rise of something like human consciousness in a machine—and Searle’s Chinese Room Argument applies and would have to be successfully resolved. If this were indeed to happen, then the objections to an AI program substituting teachers permanently in the classroom would no longer apply as rich relations between teacher and students would become a real possibility. Perhaps, new developments in AI using strategies such as (i) the observation of human expert teachers, (ii) theoretical derivation from learning theories and (iii) empirical observation of human and simulated students, which are used by Artificial Intelligence Educational Programs such as GURU and INSPIRE (Boulay and Lurkin 2015: 2; 6; cf. also Olney et al. 2012; Lepper and Woolverton 2002) will lead us in this direction.

4 Final Thoughts

In this chapter, I set out to assess the current technologization of education and the impact it has had in relations between teachers and students, as well as between students within the classroom. The position I defended was not that ‘we should not be using technology to aid teaching and learning in the classroom’ (otherwise we might still be using just oral skills or wax tablets and stylus); rather, I argued that ‘we should not overlook the importance of relations between teacher and students, and between students in the classroom’. There needs to be a balance between the technologization of education and the provision of the right conditions for rich relations to arise, which is something that educators and policy makers are not always aware. Postman (1995: 171; cited in Laura and Chapman 2009: 293) noted that the introduction of computers and technology in the classroom is an imperative, but when asked the question ‘“[w]hy should we do this?”’, answer that it is ‘“[t]o make learning more efficient and more interesting”. Such an answer is considered entirely adequate, since … efficiency and interest need no justification. It is, therefore, not usually noticed that this answer does not address the question “What is learning for?” “Efficiency and interest” is a technical answer, an answer about means, not ends; and it offers no pathway to a consideration of educational philosophy’. This is to say, that education is not solely for efficiency or market sake. These are pragmatic issues that must be considered but there is much more to education. Education is directly connected to the psychological, social and political facets of the human being, which can only be truly fulfilled by Bildung, and not merely Erziehung.